Azure Synapse to Microsoft Fabric Migration
Softree modernized an Azure Synapse environment by migrating data workloads, pipelines, Spark notebooks, OneLake, and Power BI dependencies to Microsoft Fabric.
Business Process Challenges
The client operated an Azure Synapse Analytics environment supporting data warehousing, data integration, Spark-based processing, and business intelligence. Over time, the environment expanded across dedicated SQL pools, pipelines, notebooks, lake storage, and Power BI reporting.
The separate components created operational complexity, with dependencies across data movement, orchestration, transformation, warehousing, and reporting that needed to be understood before migration.
Key challenges included:
- Assessing compatibility of existing warehouse objects and SQL logic.
- Migrating Synapse pipelines with their linked services, parameters, schedules, and dependencies.
- Updating Spark notebooks, libraries, configurations, and data-access patterns.
- Defining an appropriate OneLake strategy for existing lake data.
- Validating Power BI semantic models, reports, refreshes, and security.
- Minimizing business disruption during cutover.
Our Strategic Approach
Softree followed a workload-by-workload migration approach to move the existing Azure Synapse environment toward a Microsoft Fabric-based data platform.
Assess & Prioritize
Softree inventoried Synapse workspaces, SQL pools, schemas, tables, views, stored procedures, pipelines, triggers, notebooks, Spark dependencies, lake paths, and Power BI assets. Workloads were assessed based on complexity, business criticality, dependencies, data volume, and execution patterns.
Modernize the Data Warehouse
Dedicated SQL pool workloads were assessed for Fabric Warehouse compatibility. Schemas, tables, views, stored procedures, data types, SQL syntax, and performance patterns were reviewed before migration. Data and business measures were reconciled against the source environment.
Migrate Data Pipelines
Synapse pipelines were reviewed activity by activity and upgraded or recreated using Fabric Data Factory. Connections, parameters, dependencies, triggers, retry behavior, logging, and failure handling were validated as part of the migration.
Migrate Spark Workloads
Spark notebooks were reviewed for runtime compatibility, libraries, packages, storage paths, APIs, and external dependencies. Notebook outputs, schemas, record counts, and business calculations were validated against representative data.
Establish the OneLake Foundation
OneLake was established as the target data foundation, with migration decisions made around copying, reusing, or using shortcuts for existing lake data. Workspace boundaries, permissions, naming, lineage, and data ownership were also considered.
Integrate Power BI
Power BI semantic models and reports were mapped to the target Fabric data structures. Relationships, measures, refresh settings, security rules, permissions, filters, drill paths, and key business metrics were validated.
Test & Cut Over
Softree performed functional testing, data reconciliation, performance benchmarking, and user acceptance testing before a controlled production cutover. Pipeline runs, notebook failures, refresh history, capacity consumption, and user-reported issues were monitored during stabilization.
How we delivered it.
Explore the Solution Through visuals
What changed for the client.
The migration established a structured path toward a more integrated Microsoft Fabric data engineering and analytics environment.
The expected impact areas included:
- Platform simplification through a more integrated Fabric experience.
- Improved operational visibility through centralized monitoring and clearer workload ownership.
- Greater data trust through documented reconciliation and validation of key business measures.
- Analytics continuity through validated Power BI models, reports, refreshes, and access controls.
- Reduced migration risk through workload assessment, phased deployment, acceptance criteria, and rollback planning.
- Measured performance and cost visibility through comparison of workload performance and Fabric capacity consumption.
The numbers behind the rollout.
The full integration layer.
Other engagements worth a look.
SQL Server to Microsoft Fabric Data Warehouse Migration
Modernizing an enterprise SQL Server data warehouse with Microsoft Fabric while maintaining data quality, performance, security, and reporting continuity.
Read case study→
Healthcare Billing & Denial Resolution Agent
A Singapore healthcare provider proposed an AI agent to streamline claim denial investigations, payer research, follow-ups, and specialist review.
Read case study→
Hospital Operations & Bed Management Agent
A UAE hospital network proposed an agentic AI solution to improve bed readiness, discharge coordination, patient transfers, and operational visibility.
Read case study→
Enterprise Lakehouse Power BI Migration Modernization Services
A U.S. retail and e-commerce company modernized its Azure data platform with Microsoft Fabric, unifying analytics workloads and improving data governance.
Read case study→
Healthcare Data Validation and Quality Assurance
Explore an AI-powered healthcare data validation platform designed to test data integrity across patient records, lab results, and insurance claims.
Read case study→
Tableau to Power BI Migration for Enterprise Analytics Modernization
An enterprise organization planned its Tableau-to-Power BI migration to standardize reporting, validate business calculations, and strengthen analytics governance.
Read case study→Frequently asked questions.
Build faster with a reliable offshore engineering partner.
Partner with Softree to accelerate product delivery, modernize enterprise systems, and scale with confidence.
What we offer
Offices
Bengaluru
11th Floor, Prestige Tech Park, Platina 2 · Outer Ring Rd, Kadubeesanahalli · Bengaluru, Karnataka 560087, India
Cuttack
PLOT B6-1628, SECTOR-10, CDA · Cuttack, Odisha 753014 · India
San Francisco
San Francisco, CA 94108 · United States
Got a question, challenge, or idea?
Fill out the form or pick a time on our scheduler.
30-min discovery call
Same Calendly as our booking page · instant invite